Air Pollution Patterns Research Articles

Challenges and Potentials: Environmental Assessment of Particulate Matter in Spaces Under Highway Viaducts

Under-viaduct space (UVS) is becoming an important solution to urban mobility problems, and the construction and use of high-density city center highways and elevated bridges are increasing, which has a negative impact on the UVS. Air pollution is a problem in these spaces, but research on air pollution in UVSs is lacking. To further study air pollution in UVS, this study selected a case area of a UVS in central Shanghai and investigated the spatiotemporal distribution patterns of air pollution and the influencing factors. We found that air pollution in the UVS is significantly higher than the background levels, and the higher the background levels, the greater the difference between the pollution of the UVS and the background. In terms of the impact factor, air pollution is highly correlated with the built environment and traffic flow. The research provides evidence of the exposure to air pollution in under viaducts spaces in the microenvironment.

Spatial and temporal patterns of urban air pollution in tehran with a focus on PM2.5 and associated pollutants

Understanding the spatial and temporal dynamics of air pollutants is crucial for effective urban air pollution management. This study focuses on the temporal dynamics of air quality monitoring stations (AQMSs) and the association among air pollutants, particularly PM2.5, in Tehran, Iran. Using time series clustering and the Copula model, we analyzed data from 2019 to 2022. We found that the levels and dynamics of O3 and SO2 were similar across most AQMSs and unrelated to geographical positions. CO levels and dynamics were consistent among urban and border AQMSs, with higher concentrations in urban stations. NO2 levels and dynamics varied significantly among northern AQMSs with no relationship geographical positions. PM10 levels and dynamics had a relationship with geographical positions, with western clusters having the highest and northern clusters the lowest concentrations. The dynamics of PM2.5 showed significant relationship among AQMSs in the eastern, southern, and western regions, but not in the north. We also observed that PM10 and O3 levels were higher in warm seasons, whereas CO, SO2, NO2, and PM2.5 levels were higher in cold seasons. Most pollutants, except O3, peaked during traffic hours. Notably, the significant increase in PM2.5 since spring 2021 was primarily due to PM10. Policymakers should focus on these spatial and temporal variations to improve urban air quality and public health outcomes through targeted interventions.

Ozone Induces Oxidative Stress and Inflammation in Nasal Mucosa of Rats

Background: The development of the global economy has led to changes in air pollution patterns. The haze phenomenon characterized by high concentrations of particulate matter 2.5 (PM2.5) has changed to complex pollution, and photochemical pollution characterized by ozone (O3) has become increasingly prominent. Ozone pollution and its impact on human health has become an important topic that needs to be studied urgently. Objective: To investigate the effects of ozone on oxidative stress and inflammation in the nasal mucosa of a rat model. Methods: Thirty-two healthy female Sprague–Dawley rats, eight in each group, were divided into four groups using the randomized numeric table method: normal control group (NC group), normal rats with a low level of ozone inhalation exposure (NEL group, 0.5 ppm), medium ozone inhalation exposure (NEM group, 1 ppm), and high ozone inhalation exposure (NEH group, 2 ppm). The ozone inhalation exposure groups were placed in the ozone inhalation exposure system and exposed to different concentrations of ozone for 2 h each day for 6 weeks. Nasal secretion was measured, and nasal lavage and nasal mucosa were collected. Malondialdehyde (MDA), superoxide dismutase (SOD), and glutathione peroxidase (GSH-Px) activities were measured by colorimetric assay, and the nasal mucosa was analyzed by Western blot. Western blot (WB) was used to detect the expression of NF-κB p65 nuclear protein in nasal mucosa. The mRNA expression of NF-κB target genes IL-6 and IL-8 and tumor necrosis factor-α (TNF-α) was detected by real-time quantitative PCR (qRT-PCR), and the protein content of pro-inflammatory factors IL-6, IL-8, and TNF-α was detected by ELISA in serum and nasal lavage fluid. The nasal mucosa of rats was stained with hematoxylin-eosin (HE) to observe the pathological changes in the nasal mucosa. The data were analyzed by SPSS 20.0 software. Results: The amount of nasal secretion increased significantly in all groups after ozone exposure compared with that in the NC group. The MDA content of the nasal mucosa was significantly increased in the ozone-exposed group compared with the NC group, and the activity levels of SOD and GSH-Px in the nasal mucosa were lower in the ozone-exposed group than in the NC group. The mRNA expression of IL-6, IL-8, and TNF-α in the nasal mucosa of the ozone-exposed group was elevated, and the protein content of TNF-α, IL-6, and IL-8 in the nasal lavage fluid was elevated, and the content increased with the increase in ozone concentration. The expression of NF-κB p65 intracellular protein in the nasal mucosa of each ozone-exposed group was higher than that of the normal group, and the content increased with the increase in ozone concentration. Conclusions: Ozone inhalation exposure promotes oxidative stress and the release of inflammatory factors TNF-α, IL-6, and IL-8, leading to pathological damage of the nasal mucosa, the degree of which increases with increasing concentration. This pathological process may be related to the activation of the transcription factor NF-κB by ozone in the nasal mucosa of rats, which increases the expression of its target genes.

Analysis of the Impact of Meteorological Factors on Predicting Air Quality in South Tangerang City using Random Forest Method

Air pollution has become one of the most significant environmental problems in many cities throughout the world, which can endanger public health and the environment. Understanding the impact of meteorological conditions on air quality is very important to understanding air pollution patterns. This study investigates the influence of meteorological variables on air quality predictions in South Tangerang City, Indonesia, using the Random Forest method. Modeling is carried out by building two scenarios, namely predictions using meteorological variables and predictions without meteorological variables. Prediction performance analysis is measured using MAE, MSE, RMSE, R-square, and accuracy. The accuracy results of the research show that predictions without meteorological variables provide good prediction results with a value of 86.42%, but predictions with meteorological variables have better performance with a value reaching 98.99%. The largest error values from each model were 2.58 MAE, 71.82 MSE, and 8.4747 RMSE obtained in prediction modeling without meteorological variables, while the smallest error values were obtained in prediction modeling using meteorological variables, namely 0.00, 0.01, and 0.0219, respectively, for MAE, MSE, and RMSE. This research contributes to a better understanding of the relationship between meteorology and air pollution and air quality in urban areas and helps develop targeted mitigation strategies to improve air quality and public health, especially in South Tangerang City and the surrounding area.

Synthesis of satellite and ground data provide unique perspectives for discovering the air pollution patterns: A case study in Guangdong Province, China

Existing studies have analyzed the spatio-temporal patterns of air pollutants by combining ground and satellite measurements, primarily for cross-validation purposes. However, the unique characteristics and discrepancies between satellite and ground measurements have rarely been leveraged to understand pollution patterns and identify air pollution sources. To our best knowledge, this study is the first to utilize these discrepancies to holistically analyze the spatial and temporal patterns and investigate local biomass-burning effects on the five typical air pollutants: particulate matter (PM2.5)/aerosol optical depth (AOD), carbon monoxide (CO), nitrogen dioxide (NO2), sulphur dioxide (SO2), and ozone (O3). Guangdong (GD) province was selected as a case study due to its complex air pollution sources and patterns. Ground-based analysis from 2015 to 2023 shows significant decreases in PM2.5, CO, NO2, and SO2, and a significant increase in O3 in urban areas, indicating the efficacy of stringent air pollution control policies. However, satellite analysis shows significant downtrend only in AOD, while the trends of other pollutants are almost negligible, which are likely to be evidence of industrial migration. Both measurements exhibit regular seasonal patterns for all air pollutants. In-depth time-series comparisons between ground and satellite data reveal seasonal consistency for NO2 but noticeable discrepancies for both AOD and CO, which could be attributed to urban-rural differences and local versus transported pollution sources. Spatially, AOD and NO2 exhibits the most significant regional discrepancies, followed by SO2 and CO, with higher values observed over Pearl River Delta (PRD) compared to non-PRD regions. O3 is more evenly distributed, showing more pronounced seasonal variations than regional differences. The synergetic use of satellite and ground measurements collectively verifies the significant local biomass-burning effects on the five pollutants. These findings can aid in developing more targeted air pollution control policies.

A UAV-based method for efficiently measuring the spatial pattern of multiple air pollutants in street canyons

Street canyons are fundamental units of urban spaces where individuals engage in social activities. The air quality within street canyons directly impacts the health and behavior of residents. Therefore, it is crucial to comprehensively assess and depict the actual distribution of multiple air pollutants (SO2, CO, NO2, O3, PM2.5, and PM10) in street canyons, for both investigation and validation purposes. However, an efficient and accurate method for doing so is rarely available. To address this issue, indoor and field measurements were conducted to determine the response times of these pollutants in various polluted environments, identify the optimal stay duration at individual sampling points, and optimize the sampling layout. A method based on Unmanned Aerial Vehicle (UAV) was developed to efficiently measure the spatial pattern of pollutants in street canyons and verified through field application. The results showed that the actual response times of pollutants in heavily and slightly polluted environments were less than 30 s. The recommended stay duration at an individual sampling point in an outdoor environment was determined to be 2 min, with uncertainty of 2 %. For the sampling layout, it is recommended to take 12 samples to obtain a spatial pattern of a street canyon. These samplings should be arranged at the air inlet, middle, and air outlet along the middle of the street canyon, as well as at their bottom, 20 %–30 %, 50 %–60 %, and top in the vertical direction, respectively. The UAV-based method can efficiently complete the sampling process in just 24 min.

Yearly variations of water-soluble ions over Xi'an, China: Insight into the importance contribution of nitrate to PM2.5

Water-soluble inorganic ions (WSIIs) in PM2.5 play an important role in the formation of air pollution, which in turn affects climate change and human health. The formation pathways and factors influencing WSIIs have received extensive attention. Here, we analyzed the contents of nine WSIIs in PM2.5, collected from 2015 to 2021 in Xi'an City, China, with the aim of investigating long-term atmospheric pollution changes. Sulfate (SO42−), nitrate (NO3−), and ammonium (NH4+) together contributed to 66.8%–88.1% and Ca2+ accounted for 5.1%–13.1% of total WSIIs. The relative content of SO42− exhibited a gradually decreasing trend (from 49.80% in 2015 to 29.98% in 2021), whereas NO3− was increased in the same time period (from 13.96% in 2015 to 29.92% in 2021). In addition, the nitrogen oxidation rate showed an annual increase in this period, whereas the sulfur oxidation rate decreased, and their fitted curves intersected in 2019. The key finding of this study is that the air pollution pattern in Xi'an has changed from sulfate-dominated to nitrate-dominated particles, as evidenced by the feature importance results of the random forest model. We propose that more attention should be paid to vehicle emissions and road dust as pollution sources. Overall, the findings of this study serve as a useful reference to aid relevant authorities in devising more effective policies for controlling PM2.5 pollution at its source.

Electric buses as an air pollution and meteorological observation network: Methodology and preliminary results

Many local agencies in the United States and other countries are tasked to install air pollution monitoring systems of highly accurate sensors that have high acquisition, operating, and maintenance costs. The need for expanded coverage of air quality measurements across Salt Lake County (SLCO), Utah is being met by mounting air quality and temperature sensors on an expanding fleet of battery electric buses (BEBs). Monitoring air quality from a mobile sensor network provides real-time insights into air pollution patterns at high temporal and spatial resolution. Mobile measurements contribute to assessing residents' exposure to air pollution, facilitating the implementation of cost-effective public health policies and highlighting disparities. The Electric Bus Air Quality Observation Project was launched in SLCO during July 2021 and has collected millions of observations to date. A BEB traveling at typical traffic speeds (~10 m s−1) can provide multiple measurements along city block lengths of up to ~200 m. With careful analysis that factors in the time response of the differing sensors, variability from block-to-block may be attributed to fine-scale factors (e.g., pollution and heat sources, tree shading and urban vegetation, etc.). Preliminary findings showcase the value of increased coverage and resolution. During an extreme heat event in July 2023, both the morning and afternoon temperature readings showed differences of over 6.5 °C (12 °F), primarily as an east-west gradient with similar gradients in ozone. We conclude that temperature and pollutant concentration readings, at fine spatial and temporal resolutions, will facilitate future health studies and equitable policy and mitigation strategies. Our study demonstrates that our partnerships established with governmental, non-profit, and transit agencies facilitate the successful transfer of research and development to operational real-time mobile air quality monitoring.

Characterizing the distribution pattern of traffic-related air pollutants in near-road neighborhoods.

In near-road neighborhoods, residents are more frequently exposed to traffic-related air pollution (TRAP), and they are increasingly aware of pollution levels. Given this consideration, this study adopted portable air pollutant sensors to conduct a mobile monitoring campaign in two near-road neighborhoods, one in an urban area and one in a suburban area of Shanghai, China. The campaign characterized spatiotemporal distributions of fine particulate matter (PM2.5) and black carbon (BC) to help identify appropriate mitigation measures in these near-road micro-environments. The study identified higher mean TRAP concentrations (up to 4.7-fold and 1.7-fold higher for PM2.5 and BC, respectively), lower spatial variability, and a stronger inter-pollutant correlation in winter compared to summer. The temporal variations of TRAP between peak hour and off-peak hour were also investigated. It was identified that district-level PM2.5 increments occurred from off-peak to peak hours, with BC concentrations attributed more to traffic emissions. In addition, the spatiotemporal distribution of TRAP inside neighborhoods revealed that PM2.5 concentrations presented great temporal variability but almost remained invariant in space, while the BC concentrations showed notable spatiotemporal variability. These findings provide valuable insights into the unique spatiotemporal distributions of TRAP in different near-road neighborhoods, highlighting the important role of hyperlocal monitoring in urban micro-environments to support tailored designing and implementing appropriate mitigation measures.

Federated Bayesian network approach for cross-regional air pollution classification: a case study of the Beijing-Tianjin-Hebei region.

Although machine learning methods have enabled considerable progress in air quality assessment, challenges persist regarding data privacy, cross-regional data processing, and model generalization. To address these issues, we introduce an advanced federated Bayesian network (FBN) approach. By integrating federated learning, adaptive optimization algorithms, and homomorphic encryption technologies, we substantially enhanced the efficiency and security of cross-regional air quality data processing. The novelty of this research lies in the improvements implemented in federated learning for air quality data analysis, particularly in distributed model training optimization and data consistency. Through the integration of adaptive structural modification strategies and simulated annealing immune optimization algorithms, we markedly enhanced the structural learning accuracy of the Bayesian network, resulting in a 20% improvement in prediction accuracy. Moreover, employing homomorphic encryption ensured data transmission security and confidentiality. In our Beijing-Tianjin-Hebei case study, our method demonstrated a 15% improvement in air quality classification accuracy compared to conventional methods and exhibited superior interpretability in analyzing environmental factor interactions. We quantified complex air pollution patterns across regions and found that a 30% fluctuation in the air quality index correlated with NO2 concentrations. We also observed a moderate positive correlation between specific pollutant indicators in Hebei Province and Tianjin and changes in air quality. Additionally, the FBN exhibited better operational efficiency and data confidentiality than other machine learning models in handling large-scale and multisource environmental data. Our FBN approach presents a novel perspective for environmental monitoring and assessment, vital for understanding complex air pollution patterns and formulating future ecological protection policies.

Modulate synthesis of CeMn solid solution using various alcohols for toluene catalytic oxidation: synergistic effect of Ce-Mn and reaction mechanism

A redox co-precipitation method was employed to synthesize CeMn homogeneous solid solutions, utilizing various alcohols as activating agents. Ethanol effectively orchestrated the precipitation of CeO2 and MnOx, promoting their co-growth. As a result, the CeMn-EA achieved 90 % toluene conversion at 218 ℃ (T90 =218 ℃) with a weight hourly space velocity (WHSV) of 48000 ml/(g·h). It also demonstrated high adaptability to increased WHSV, suggesting its potential for industrial-scale applications. The uniform dispersion of Ce and Mn accelerated the coupling between Ce3+/Ce4+ and Mn4+/Mn3+, engineering numerous oxygen vacancies, which enhanced the activation of gas-phase oxygen and the mobility of lattice oxygen. In situ DRIFTS confirmed that toluene oxidation accommodated both Langmuir-Hinshelwood (L-H) and Mars-van Krevelen (MvK) mechanisms, with benzoate identified as a pivotal intermediate. Enhanced oxygen mobility facilitated the cleavage of the benzene ring, which was the rate-determining step. Additionally, the introduction of H2O significantly enhanced the dissociation and adsorption of toluene and facilitated the activation of gas-phase oxygen. At higher temperatures, H2O could further activate lattice oxygen engaging in toluene oxidation. Environmental ImplicationVolatile organic compounds (VOCs) have emerged as major air pollutants due to the changes in air pollution patterns. They can act as precursors to near-surface ozone and haze. Toluene, a typical VOC, is primarily released from anthropogenic sources and poses significant risks to human health and the environment. Ce-based catalysts have been demonstrated efficiency in toluene oxidation due to their excellent oxygen storage and release properties. This study synthesized CeMn homogeneous solid solutions utilizing various alcohols as activating agents, which possessed abundant oxygen vacancies and optimum oxygen activation capacity to oxidize toluene in time.

Health impacts of lifestyle and ambient air pollution patterns on all-cause mortality: a UK Biobank cohort study

BackgroundExtensive evidence indicates that both lifestyle factors and air pollution are strongly associated with all-cause mortality. However, little studies in this field have integrated these two factors in order to examine their relationship with mortality and explore potential interactions.MethodsA cohort of 271,075 participants from the UK Biobank underwent analysis. Lifestyles in terms of five modifiable factors, namely smoking, alcohol consumption, physical activity, diet, and sleep quality, were classified as unhealthy (0–1 score), general (2–3 score), and healthy (4–5 score). Air pollution, including particle matter with a diameter ≤ 2.5 μm (PM2.5), particulate matter with a diameter ≤ 10 μm (PM10), particulate matter with a diameter 2.5–10 μm (PM2.5−10), nitrogen dioxide (NO2), and nitrogen oxides (NOx), was divided into three levels (high, moderate, and low) using Latent Profile Analysis (LPA). Cox proportional hazard regression analysis was performed to examine the links between lifestyle, air pollution, and all-cause mortality before and after adjustment for potential confounders. Restricted cubic spline curves featuring three knots were incorporated to determine nonlinear relationships. The robustness of the findings was assessed via subgroup and sensitivity analyses.ResultsWith unhealthy lifestyles have a significantly enhanced risk of death compared to people with general lifestyles (HR = 1.315, 95% CI, 1.277–1.355), while people with healthy lifestyles have a significantly lower risk of death (HR = 0.821, 95% CI, 0.785–0.858). Notably, the difference in risk between moderate air pollution and mortality risk remained insignificant (HR = 0.993, 95% CI, 0.945–1.044). High air pollution, on the other hand, was independently linked to increased mortality risk as compared to low air pollution (HR = 1.162, 95% CI, 1.124–1.201). The relationship between NOx, PM10, and PM2.5−10 and all-cause mortality was found to be nonlinear (p for nonlinearity < 0.05). Furthermore, no significant interaction was identified between lifestyle and air pollution with respect to all-cause mortality.ConclusionsExposure to ambient air pollution elevated the likelihood of mortality from any cause, which was impacted by individual lifestyles. To alleviate this hazard, it is crucial for authorities to escalate environmental interventions, while individuals should proactively embrace and sustain healthy lifestyles.

Exploring Spatial–Temporal Patterns of Air Pollution Concentration and Their Relationship with Land Use

Understanding the spatial–temporal patterns of air pollution is crucial for mitigation strategies, a task fostered nowadays by the generation of continuous concentration maps by remote sensing technologies. We applied spatial modelling to analyze such spatial–temporal patterns in Lombardy, Italy, one of the most polluted regions in Europe. We conducted monthly spatial autocorrelation (global and local) of the daily average concentrations of PM2.5, PM10, O3, NO2, SO2, and CO from 2016 to 2020, using 10 × 10 km satellite data from the Copernicus Atmosphere Monitoring Service (CAMS), aggregated on districts of approximately 100,000 population. Land-use classes were computed on identified clusters, and the significance of the differences was evaluated through the Wilcoxon rank-sum test with Bonferroni correction. The global Moran’s I autocorrelation was overall high (&gt;0.6), indicating a strong clustering. The local autocorrelation revealed high–high clusters of PM2.5 and PM10 in the central urbanized zones in winter (January–December), and in the agrarian southern districts in summer and autumn (May–October). The temporal decomposition showed that values of PMs are particularly high in winter. Low–low clusters emerged in the northern districts for all the pollutants except O3. Seasonal peaks for O3 occurred in the summer months, with high–high clusters mostly in the hilly and mildly urban districts in the northwest. These findings elaborate the spatial patterns of air pollution concentration, providing insights for effective land-use-based pollution management strategies.

Analysis of the synergistic benefits of typical technologies for pollution reduction and carbon reduction in the iron and steel industry in the Beijing–Tianjin–Hebei region

With its high energy consumption and pollutant emissions, the iron and steel industry is a significant source of air pollution and carbon emissions in the Beijing–Tianjin–Hebei (BTH) region. To improve air quality and reduce greenhouse gas emissions, a series of policies involving ultra-low emission, synergistic reduction of pollution, and carbon application have been implemented in the region. This study has assessed air pollutant and CO2 emission patterns in the iron and steel industry of the region by employing co-control effects coordinate system, marginal abatement cost curve, and numerical modeling, along with the synergistic benefits of typical technologies. The results have demonstrated that: (1) the intensive production activities pertinent to iron and steel enterprises has contributed greatly to the emission in Tangshan and Handan, where the sintering process is the main source of SO2, NOx, PM2.5, and CO, accounting for 64.86%, 55.15%, 29.98%, and 46.43% of the total emissions, respectively. (2) Among the typical pollution control and reduction measures, industrial restructuring and adjustment of the energy-resource structure have led to the greatest effects on emission reduction. Technologies exhibiting great potential in emission reduction and high-cost efficiency such as Blast Furnace Top Gas Recovery Turbine Unit (TRT) need to be promoted. (3) In Tangshan city with the highest level of steel production, the iron and steel production activities contributed to the concentration of 30.51% of PM2.5, 50.67% of SO2, and 42.54% of NO2 during the non-heating period. During the heating period, pollutants pertinent to the combustion of fossil energy for heating have increased, while iron and steel induced emissions have decreased to 23.7%, 34.32%, and 29.13%, respectively. By 2030, it is speculated that the contribution of the iron and steel industry to air quality will be significantly decreased as result of successful implementation of ultra-low emission policies and typical synergistic reduction technologies.

How does air pollution affect floating population in metropolitan city: embedding-based approach

AbstractRecently, the air pollution has been seriously regarded in the urban environment. Particularly, the substantial relationship between the air pollution and the daily movements of citizens has not been sufficiently investigated yet. This study attempts to empirically identify the patterns of air pollution using association rule mining from Seoul, the metropolitan city in South Korea. As a result, 214 patterns on air pollution are discovered, and those are embedded into vectors based on Doc2Vec technique. Then, this paper further examines how the movement of citizens reacts to the discovered patterns of air pollution by deploying the linear regression on the floating population with emphasis on the walk-traffic. Specifically, the walk-traffic is categorized into 14 categories by gender and age group, and the effects of air pollution patterns on each subgroup walk traffic were analyzed. Findings of this paper provide the empirical evidences on the estimated air pollution sensitivity by generation and gender to researchers and practitioners. This paper has the contribution on newly proposing the methodological framework for further managing the air pollution in the urban environment. Graphical abstract

Decadal Trends in Ambient Air Pollutants and Their Association with COPD and Lung Cancer in Upper Northern Thailand: 2013-2022.

Air pollution in upper northern Thailand raises health concerns. This study examined trends and associations between air pollutants and respiratory diseases, focusing on COPD and lung cancer during haze (December-May) and non-haze (June-November) seasons in upper northern Thailand from 2013 to 2022. This study utilized data from the Pollution Control Department and Chiang Mai Provincial Public Health. The key air pollutants included PM10, PM2.5, SO2, NO2, CO, and O3. Respiratory disease data included fatality rates for lung cancer and COPD and the re-admission rate for COPD. Results indicated peak air pollutant levels and COPD re-admission rates in March, with PM2.5 concentrations exceeding air quality standards from January to April. During haze periods, COPD fatality and re-admission rates significantly increased (mean difference: 0.43 and 4.23 per 1000-case population, respectively; p < 0.001), while lung cancer fatality rates were higher without statistical significance. Pearson correlation analysis found positive correlations between PM10, PM2.5, O3, and NO2 concentrations and COPD re-admission and fatality rates at 0-1 month lag times, with a declining trend observed at subsequent lag intervals of 2 to 3 months. Overall, this study highlights the predictable pattern of air pollution in the region, correlating with higher COPD fatality and re-admission rates.

PM&lt;sub&gt;2.5&lt;/sub&gt; air pollution trends and patterns in Kandy, Sri Lanka

Air pollution is the leading environmental hazard that has devastating health effects on human health. Identification of trends and patterns of air pollution is crucial in a specific geographic area to inform and implement effective control and mitigation measurements. Standard air pollution monitoring networks are costly for most low-and middle-income countries. The aim of this study was to investigate the variations in the mean concentrations of air pollutants that occur at various times of the day, days of the week, months of the year, and seasons during the period of January 2019 to December 2019 in Kandy, Sri Lanka using low-cost sensors. A regression analysis with dummy variables was used to model the relationship between the concentrations of air pollutants and categorical independent variables, the time of day and day of the week. Considering temporal variations, the study found that pollution is highest in the early morning and evening, and lowest in the afternoon. The worst days for the air quality were Wednesdays and Thursdays, while Sundays had the lowest PM2.5 pollution. The analysis of air quality variations by month showed that December had the lowest air pollution of the examined months. The variation in PM2.5 was greatly influenced by wind speed, wind direction, and rainfall patterns. The study emphasized the importance of long-term consistently management and monitoring of air pollution levels.

A spatial analysis of ozone and PM2.5 distribution for assessing design factors of healthy buildings

Indoor air quality (IAQ) represents one of the significant challenges in healthy building design, with thermal comfort and daylighting being primary design objectives. This study aims to demonstrate the potential of energy-independent building infrastructure as a passive design solution to mitigate the impact of outdoor air pollution. This paper presents experimental and numerical investigations to establish the relationship between design factors (e.g., building orientation, geometry, and window opening location) and the spatial distribution of ozone and fine particulate matter (PM2.5) in residential houses. Our case study involves experimentally validated computational fluid dynamics (CFD) simulations for typical rowhouse archetypes in one of the United States' most polluted cities. Our findings indicate that higher ozone levels are observed on upper floors, posing a significant exposure risk at a breathing zone height of 1.7 m. In contrast, PM2.5 levels are most concerning at a height of 1 m, directly impacting children's health. Alarmingly, future trends of ozone pollution suggest that these risks could be almost three times higher compared to current conditions. Given the distinctive diurnal patterns of outdoor air pollution, the study also finds that the strategic placement of windows for nighttime air flushing, specifically in the upper two-thirds of the building, can improve IAQ. Our analysis of the spatial placement of the air purifier reveals that the optimal location is at the middle center of the windward wall. Our work underscores the potential of building design features in mitigating pollution exposure in indoor environments, emphasizing the importance of architectural solutions in public health.

Seasonal extreme temperatures and short-term fine particulate matter increases pediatric respiratory healthcare encounters in a sparsely populated region of the intermountain western United States

BackgroundWestern Montana, USA, experiences complex air pollution patterns with predominant exposure sources from summer wildfire smoke and winter wood smoke. In addition, climate change related temperatures events are becoming more extreme and expected to contribute to increases in hospital admissions for a range of health outcomes. Evaluating while accounting for these exposures (air pollution and temperature) that often occur simultaneously and may act synergistically on health is becoming more important.MethodsWe explored short-term exposure to air pollution on children’s respiratory health outcomes and how extreme temperature or seasonal period modify the risk of air pollution-associated healthcare events. The main outcome measure included individual-based address located respiratory-related healthcare visits for three categories: asthma, lower respiratory tract infections (LRTI), and upper respiratory tract infections (URTI) across western Montana for ages 0–17 from 2017–2020. We used a time-stratified, case-crossover analysis with distributed lag models to identify sensitive exposure windows of fine particulate matter (PM2.5) lagged from 0 (same-day) to 14 prior-days modified by temperature or season.ResultsFor asthma, increases of 1 µg/m3 in PM2.5 exposure 7–13 days prior a healthcare visit date was associated with increased odds that were magnified during median to colder temperatures and winter periods. For LRTIs, 1 µg/m3 increases during 12 days of cumulative PM2.5 with peak exposure periods between 6–12 days before healthcare visit date was associated with elevated LRTI events, also heightened in median to colder temperatures but no seasonal effect was observed. For URTIs, 1 unit increases during 13 days of cumulative PM2.5 with peak exposure periods between 4–10 days prior event date was associated with greater risk for URTIs visits that were intensified during median to hotter temperatures and spring to summer periods.ConclusionsDelayed, short-term exposure increases of PM2.5 were associated with elevated odds of all three pediatric respiratory healthcare visit categories in a sparsely population area of the inter-Rocky Mountains, USA. PM2.5 in colder temperatures tended to increase instances of asthma and LRTIs, while PM2.5 during hotter periods increased URTIs.

Quantifying the impacts of Canadian wildfires on regional air pollution networks

Wildfire smoke greatly impacts regional atmospheric systems, causing changes in the behavior of pollution. However, the impacts of wildfire smoke on pollution behavior are not easily quantifiable due to the complex nature of atmospheric systems. Air pollution correlation networks have been used to quantify air pollution behavior during ambient conditions. However, it is unknown how extreme pollution events impact these networks. Therefore, we propose a multidimensional air pollution correlation network framework to quantify the impacts of wildfires on air pollution behavior. The impacts are quantified by comparing two time periods, one during the 2023 Canadian wildfires and one during normal conditions with two complex network types for each period. In this study, the value network represents PM2.5 concentrations and the rate network represents the rate of change of PM2.5 concentrations. Wildfires' impacts on air pollution behavior are captured by structural changes in the networks. The wildfires caused a discontinuous phase transition during percolation in both network types which represents non-random organization of the most significant spatiotemporal correlations. Additionally, wildfires caused changes to the connectivity of stations leading to more interconnected networks with different influential stations. During the wildfire period, highly polluted areas are more likely to form connections in the network, quantified by an 86 % and 19 % increase in the connectivity of the value and rate networks respectively compared to the normal period. In this study, we create novel understandings of the impacts of wildfires on air pollution correlation networks, show how our method can create important insights into air pollution patterns, and discuss potential applications of our methodologies. This study aims to enhance capabilities for wildfire smoke exposure mitigation and response strategies.